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Journal articles on the topic "The k-average method"

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İŞLEYEN, Şakir. "Clustering Analysis of Employment Sectors According to OECD Countries Using the K-Average Method." International Journal of Contemporary Economics and Administrative Sciences 11, no. 1 (2021): 093–105. https://doi.org/10.5281/zenodo.5136506.

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<strong>Abstract</strong> In addition to being one of the important parameters showing the welfare level of the countries, employment shows the economic development of the countries in which sector is concentrated. The existence of industry-based employment in developed or developing countries supports this situation. In addition, the resources of countries direct the employment policy of that country. It is stated in the literature that there is generally employment in this field in countries with high agricultural resources. In this study, the employment data of 36 OECD countries between 1991 and 2019 were analysed using the Cluster analysis K-Average Method, which was obtained from the official web site of the World Bank. According to the employment data in Agriculture, Industry and Service sectors, it was analysed in which cluster OECD countries are located and whether the variables show a meaningful clustering.
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Angelina, Lisa Harsyiah, and Nur Asmita Purnamasari. "PERBANDINGAN METODE AVERAGE LINKAGE DAN K-MEANS DALAM MENGELOMPOKKAN PERSEBARAN PENYAKIT MULUT DAN KUKU DI INDONESIA." Fraction: Jurnal Teori dan Terapan Matematika 4, no. 2 (2024): 49–57. https://doi.org/10.33019/fraction.v4i2.63.

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The purpose of this study was to analyze the spread of Foot and Mouth Disease (FMD in Indonesia by using two different methods: average linkage and k-means. In addition, this study also aimed to determine the most effective method of classifying the distribution of FMD in Indonesia between the two methods used. The results of cluster validation showed that the optimal number of clusters formed in the average linkage method was 4, while in the k-means method, there were 3 clusters. The grouping with the average linkage method was better than the results of classifying with the k-means method, as the standard deviation ratio in the average linkage method was smaller at 0,035, compared to 0,258 in the k-means method. Therefore, it was concluded that the average linkage method was better than the k-means method in classifying the distribution of FMD in Indonesia.
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Voznyi, Yaroslav, Mariia Nazarkevych, Volodymyr Hrytsyk, Nataliia Lotoshynska, and Bohdana Havrysh. "DESIGN OF BIOMETRIC PROTECTION AUTHENTIFICATION SYSTEM BASED ON K-AVERAGE METHOD." Cybersecurity: Education, Science, Technique 12, no. 4 (2021): 85–95. http://dx.doi.org/10.28925/2663-4023.2021.12.8595.

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The method of biometric identification, designed to ensure the protection of confidential information, is considered. The method of classification of biometric prints by means of machine learning is offered. One of the variants of the solution of the problem of identification of biometric images on the basis of the k-means algorithm is given. Marked data samples were created for learning and testing processes. Biometric fingerprint data were used to establish identity. A new fingerprint scan that belongs to a particular person is compared to the data stored for that person. If the measurements match, the statement that the person has been identified is true. Experimental results indicate that the k-means method is a promising approach to the classification of fingerprints. The development of biometrics leads to the creation of security systems with a better degree of recognition and with fewer errors than the security system on traditional media. Machine learning was performed using a number of samples from a known biometric database, and verification / testing was performed with samples from the same database that were not included in the training data set. Biometric fingerprint data based on the freely available NIST Special Database 302 were used to establish identity, and the learning outcomes were shown. A new fingerprint scan that belongs to a particular person is compared to the data stored for that person. If the measurements match, the statement that the person has been identified is true. The machine learning system is built on a modular basis, by forming combinations of individual modules scikit-learn library in a python environment.
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Sihombing, Pardomuan Robinson. "Implementation of K-Means and K-Medians Clustering in Several Countries Based on Global Innovation Index (GII) 2018." Advance Sustainable Science, Engineering and Technology 3, no. 1 (2021): 0210107. http://dx.doi.org/10.26877/asset.v3i1.8461.

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The Global Innovation Index (GII) is an instrument to assess the ranking of innovation capabilities of all countries. The sub-index of the GII has seven enabler pillars: Institutions, Human Capital and Research, Infrastructure, Market sophistication, Business Sophistication, Knowledge and Technology Outputs, and Creative Outputs. The k-means method and k-medians method are methods for cluster countries based on GII. Cluster 1 in k-means method consists of 48 Countries, Cluster 2 consists of 45 Countries and Cluster 3 consists of 33 Countries and has the average value of seven variables are the highest. Cluster 1 in k-medians method consists of 33 Countries and has the average value of seven variables are the highest., Cluster 2 consists of 53 Countries and Cluster 3 consists of 40 Countries. The result clustering with using k-means method and k-medians method showed that k-medians is better than k-means method because the variance value of k-medians is smaller than k-means.
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Siregar, Hotmaida Lestari, Muhammad Zarlis, and Syahril Efendi. "Cluster Analysis using K-Means and K-Medoids Methods for Data Clustering of Amil Zakat Institutions Donor." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 2 (2023): 668. http://dx.doi.org/10.30865/mib.v7i2.5315.

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Cluster analysis is a multivariate analysis method whose purpose is to classify an object into a group based on certain characteristics. In cluster analysis, determining the number of initial clusters is very important so that the resulting clusters are also optimal. In this study, an analysis of the most optimal number of clusters for data classification will be carried out using the K-Means and K-Medoids methods. The data were analyzed using the RFM model and a comparative analysis was carried out based on the DBI value and cluster compactness which was assessed from the average silhouette score. The K-Means method produces the smallest DBI value of 0.485 and the highest average silhouette score value of 0.781 at k=6, while the K-Medoids method produces the smallest DBI value of 1.096 and the highest average silhouette score value of 0.517 at k=3. The results show that the best method for data clustering donations Amil Zakat Institutions is using the K-Means method with an optimal number of clusters of 6 clusters.
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Dhanabal, S., and S. Chandramathi. "Enhancing clustering accuracy by finding initial centroid using k-minimum-average-maximum method." International Journal of Information and Communication Technology 11, no. 2 (2017): 260. http://dx.doi.org/10.1504/ijict.2017.086252.

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Dhanabal, S., and S. Chandramathi. "Enhancing clustering accuracy by finding initial centroid using k-minimum-average-maximum method." International Journal of Information and Communication Technology 11, no. 2 (2017): 260. http://dx.doi.org/10.1504/ijict.2017.10007027.

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Gabrovšek, Boštjan, Tina Novak, Janez Povh, Darja Rupnik Poklukar, and Janez Žerovnik. "Multiple Hungarian Method for k-Assignment Problem." Mathematics 8, no. 11 (2020): 2050. http://dx.doi.org/10.3390/math8112050.

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The k-assignment problem (or, the k-matching problem) on k-partite graphs is an NP-hard problem for k≥3. In this paper we introduce five new heuristics. Two algorithms, Bm and Cm, arise as natural improvements of Algorithm Am from (He et al., in: Graph Algorithms And Applications 2, World Scientific, 2004). The other three algorithms, Dm, Em, and Fm, incorporate randomization. Algorithm Dm can be considered as a greedy version of Bm, whereas Em and Fm are versions of local search algorithm, specialized for the k-matching problem. The algorithms are implemented in Python and are run on three datasets. On the datasets available, all the algorithms clearly outperform Algorithm Am in terms of solution quality. On the first dataset with known optimal values the average relative error ranges from 1.47% over optimum (algorithm Am) to 0.08% over optimum (algorithm Em). On the second dataset with known optimal values the average relative error ranges from 4.41% over optimum (algorithm Am) to 0.45% over optimum (algorithm Fm). Better quality of solutions demands higher computation times, thus the new algorithms provide a good compromise between quality of solutions and computation time.
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Safitri, Elsa Maulida. "Clustering Study Of Hospitals In Bojonegoro Based On Health Workers With K-Means And K-Medoids Methods." Jurnal Statistika dan Komputasi 4, no. 2 (2024): 92–102. https://doi.org/10.32665/statkom.v4i2.3592.

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Background: Hospitals are institutions that provide inpatient care for the sick. In Bojonegoro, hospital services are considered adequate. However, a shortage of nurses often requires patients' families to assist with care. Objective: This research aims to compare clustering methods to find the best method that can be applied to cluster hospitals based on the type of health workers. Methods: This study uses two clustering methods, namely K-Means and K-Medoids Clustering, which are compared to determine the best method. The data source used is secondary data, which consists of the number of medical staff for each medical position, obtained from the Satu Data Bojonegoro website in 2020. Results: The K-means method proved to be the best for grouping healthcare workforce data. Its average within-cluster distance value is -6.763, the closest to zero. The K-means method resulted in 4 clusters. These include cluster_0 (3 hospitals), cluster_1 (1 hospital), cluster_2 (1 hospital), and cluster_3 (5 hospitals). Conclusion: The clustering results show that K-Means with 4 clusters is the best method, with Cluster_0 and Cluster_3 having below-average health workers, and Cluster_1 and Cluster_2 having above-average health workers, with Cluster_2 having the highest and Cluster_3 the lowest number of health workers in Bojonegoro.
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Safitri, Elsa Maulida. "Clustering Study Of Hospitals In Bojonegoro Based On Health Workers With K-Means And K-Medoids Methods." Jurnal Statistika dan Komputasi 3, no. 2 (2024): 92–102. https://doi.org/10.32665/statkom.v3i2.3592.

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Background: Hospitals are institutions that provide inpatient care for the sick. In Bojonegoro, hospital services are considered adequate. However, a shortage of nurses often requires patients' families to assist with care. Objective: This research aims to compare clustering methods to find the best method that can be applied to cluster hospitals based on the type of health workers. Methods: This study uses two clustering methods, namely K-Means and K-Medoids Clustering, which are compared to determine the best method. The data source used is secondary data, which consists of the number of medical staff for each medical position, obtained from the Satu Data Bojonegoro website in 2020. Results: The K-means method proved to be the best for grouping healthcare workforce data. Its average within-cluster distance value is -6.763, the closest to zero. The K-means method resulted in 4 clusters. These include cluster_0 (3 hospitals), cluster_1 (1 hospital), cluster_2 (1 hospital), and cluster_3 (5 hospitals). Conclusion: The clustering results show that K-Means with 4 clusters is the best method, with Cluster_0 and Cluster_3 having below-average health workers, and Cluster_1 and Cluster_2 having above-average health workers, with Cluster_2 having the highest and Cluster_3 the lowest number of health workers in Bojonegoro.
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Books on the topic "The k-average method"

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Henriksen, Niels Engholm, and Flemming Yssing Hansen. Rate Constants, Reactive Flux. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805014.003.0005.

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This chapter discusses a direct approach to the calculation of the rate constant k(T) that bypasses the detailed state-to-state reaction cross-sections. The method is based on the calculation of the reactive flux across a dividing surface on the potential energy surface. Versions based on classical as well as quantum mechanics are described. The classical version and its relation to Wigner’s variational theorem and recrossings of the dividing surface is discussed. Neglecting recrossings, an approximate result based on the calculation of the classical one-way flux from reactants to products is considered. Recrossings can subsequently be included via a transmission coefficient. An alternative exact expression is formulated based on a canonical average of the flux time-correlation function. It concludes with the quantum mechanical definition of the flux operator and the derivation of a relation between the rate constant and a flux correlation function.
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Skiba, Grzegorz. Fizjologiczne, żywieniowe i genetyczne uwarunkowania właściwości kości rosnących świń. The Kielanowski Institute of Animal Physiology and Nutrition, Polish Academy of Sciences, 2020. http://dx.doi.org/10.22358/mono_gs_2020.

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Bones are multifunctional passive organs of movement that supports soft tissue and directly attached muscles. They also protect internal organs and are a reserve of calcium, phosphorus and magnesium. Each bone is covered with periosteum, and the adjacent bone surfaces are covered by articular cartilage. Histologically, the bone is an organ composed of many different tissues. The main component is bone tissue (cortical and spongy) composed of a set of bone cells and intercellular substance (mineral and organic), it also contains fat, hematopoietic (bone marrow) and cartilaginous tissue. Bones are a tissue that even in adult life retains the ability to change shape and structure depending on changes in their mechanical and hormonal environment, as well as self-renewal and repair capabilities. This process is called bone turnover. The basic processes of bone turnover are: • bone modeling (incessantly changes in bone shape during individual growth) following resorption and tissue formation at various locations (e.g. bone marrow formation) to increase mass and skeletal morphology. This process occurs in the bones of growing individuals and stops after reaching puberty • bone remodeling (processes involve in maintaining bone tissue by resorbing and replacing old bone tissue with new tissue in the same place, e.g. repairing micro fractures). It is a process involving the removal and internal remodeling of existing bone and is responsible for maintaining tissue mass and architecture of mature bones. Bone turnover is regulated by two types of transformation: • osteoclastogenesis, i.e. formation of cells responsible for bone resorption • osteoblastogenesis, i.e. formation of cells responsible for bone formation (bone matrix synthesis and mineralization) Bone maturity can be defined as the completion of basic structural development and mineralization leading to maximum mass and optimal mechanical strength. The highest rate of increase in pig bone mass is observed in the first twelve weeks after birth. This period of growth is considered crucial for optimizing the growth of the skeleton of pigs, because the degree of bone mineralization in later life stages (adulthood) depends largely on the amount of bone minerals accumulated in the early stages of their growth. The development of the technique allows to determine the condition of the skeletal system (or individual bones) in living animals by methods used in human medicine, or after their slaughter. For in vivo determination of bone properties, Abstract 10 double energy X-ray absorptiometry or computed tomography scanning techniques are used. Both methods allow the quantification of mineral content and bone mineral density. The most important property from a practical point of view is the bone’s bending strength, which is directly determined by the maximum bending force. The most important factors affecting bone strength are: • age (growth period), • gender and the associated hormonal balance, • genotype and modification of genes responsible for bone growth • chemical composition of the body (protein and fat content, and the proportion between these components), • physical activity and related bone load, • nutritional factors: – protein intake influencing synthesis of organic matrix of bone, – content of minerals in the feed (CA, P, Zn, Ca/P, Mg, Mn, Na, Cl, K, Cu ratio) influencing synthesis of the inorganic matrix of bone, – mineral/protein ratio in the diet (Ca/protein, P/protein, Zn/protein) – feed energy concentration, – energy source (content of saturated fatty acids - SFA, content of polyun saturated fatty acids - PUFA, in particular ALA, EPA, DPA, DHA), – feed additives, in particular: enzymes (e.g. phytase releasing of minerals bounded in phytin complexes), probiotics and prebiotics (e.g. inulin improving the function of the digestive tract by increasing absorption of nutrients), – vitamin content that regulate metabolism and biochemical changes occurring in bone tissue (e.g. vitamin D3, B6, C and K). This study was based on the results of research experiments from available literature, and studies on growing pigs carried out at the Kielanowski Institute of Animal Physiology and Nutrition, Polish Academy of Sciences. The tests were performed in total on 300 pigs of Duroc, Pietrain, Puławska breeds, line 990 and hybrids (Great White × Duroc, Great White × Landrace), PIC pigs, slaughtered at different body weight during the growth period from 15 to 130 kg. Bones for biomechanical tests were collected after slaughter from each pig. Their length, mass and volume were determined. Based on these measurements, the specific weight (density, g/cm3) was calculated. Then each bone was cut in the middle of the shaft and the outer and inner diameters were measured both horizontally and vertically. Based on these measurements, the following indicators were calculated: • cortical thickness, • cortical surface, • cortical index. Abstract 11 Bone strength was tested by a three-point bending test. The obtained data enabled the determination of: • bending force (the magnitude of the maximum force at which disintegration and disruption of bone structure occurs), • strength (the amount of maximum force needed to break/crack of bone), • stiffness (quotient of the force acting on the bone and the amount of displacement occurring under the influence of this force). Investigation of changes in physical and biomechanical features of bones during growth was performed on pigs of the synthetic 990 line growing from 15 to 130 kg body weight. The animals were slaughtered successively at a body weight of 15, 30, 40, 50, 70, 90, 110 and 130 kg. After slaughter, the following bones were separated from the right half-carcass: humerus, 3rd and 4th metatarsal bone, femur, tibia and fibula as well as 3rd and 4th metatarsal bone. The features of bones were determined using methods described in the methodology. Describing bone growth with the Gompertz equation, it was found that the earliest slowdown of bone growth curve was observed for metacarpal and metatarsal bones. This means that these bones matured the most quickly. The established data also indicate that the rib is the slowest maturing bone. The femur, humerus, tibia and fibula were between the values of these features for the metatarsal, metacarpal and rib bones. The rate of increase in bone mass and length differed significantly between the examined bones, but in all cases it was lower (coefficient b &lt;1) than the growth rate of the whole body of the animal. The fastest growth rate was estimated for the rib mass (coefficient b = 0.93). Among the long bones, the humerus (coefficient b = 0.81) was characterized by the fastest rate of weight gain, however femur the smallest (coefficient b = 0.71). The lowest rate of bone mass increase was observed in the foot bones, with the metacarpal bones having a slightly higher value of coefficient b than the metatarsal bones (0.67 vs 0.62). The third bone had a lower growth rate than the fourth bone, regardless of whether they were metatarsal or metacarpal. The value of the bending force increased as the animals grew. Regardless of the growth point tested, the highest values were observed for the humerus, tibia and femur, smaller for the metatarsal and metacarpal bone, and the lowest for the fibula and rib. The rate of change in the value of this indicator increased at a similar rate as the body weight changes of the animals in the case of the fibula and the fourth metacarpal bone (b value = 0.98), and more slowly in the case of the metatarsal bone, the third metacarpal bone, and the tibia bone (values of the b ratio 0.81–0.85), and the slowest femur, humerus and rib (value of b = 0.60–0.66). Bone stiffness increased as animals grew. Regardless of the growth point tested, the highest values were observed for the humerus, tibia and femur, smaller for the metatarsal and metacarpal bone, and the lowest for the fibula and rib. Abstract 12 The rate of change in the value of this indicator changed at a faster rate than the increase in weight of pigs in the case of metacarpal and metatarsal bones (coefficient b = 1.01–1.22), slightly slower in the case of fibula (coefficient b = 0.92), definitely slower in the case of the tibia (b = 0.73), ribs (b = 0.66), femur (b = 0.59) and humerus (b = 0.50). Bone strength increased as animals grew. Regardless of the growth point tested, bone strength was as follows femur &gt; tibia &gt; humerus &gt; 4 metacarpal&gt; 3 metacarpal&gt; 3 metatarsal &gt; 4 metatarsal &gt; rib&gt; fibula. The rate of increase in strength of all examined bones was greater than the rate of weight gain of pigs (value of the coefficient b = 2.04–3.26). As the animals grew, the bone density increased. However, the growth rate of this indicator for the majority of bones was slower than the rate of weight gain (the value of the coefficient b ranged from 0.37 – humerus to 0.84 – fibula). The exception was the rib, whose density increased at a similar pace increasing the body weight of animals (value of the coefficient b = 0.97). The study on the influence of the breed and the feeding intensity on bone characteristics (physical and biomechanical) was performed on pigs of the breeds Duroc, Pietrain, and synthetic 990 during a growth period of 15 to 70 kg body weight. Animals were fed ad libitum or dosed system. After slaughter at a body weight of 70 kg, three bones were taken from the right half-carcass: femur, three metatarsal, and three metacarpal and subjected to the determinations described in the methodology. The weight of bones of animals fed aa libitum was significantly lower than in pigs fed restrictively All bones of Duroc breed were significantly heavier and longer than Pietrain and 990 pig bones. The average values of bending force for the examined bones took the following order: III metatarsal bone (63.5 kg) &lt;III metacarpal bone (77.9 kg) &lt;femur (271.5 kg). The feeding system and breed of pigs had no significant effect on the value of this indicator. The average values of the bones strength took the following order: III metatarsal bone (92.6 kg) &lt;III metacarpal (107.2 kg) &lt;femur (353.1 kg). Feeding intensity and breed of animals had no significant effect on the value of this feature of the bones tested. The average bone density took the following order: femur (1.23 g/cm3) &lt;III metatarsal bone (1.26 g/cm3) &lt;III metacarpal bone (1.34 g / cm3). The density of bones of animals fed aa libitum was higher (P&lt;0.01) than in animals fed with a dosing system. The density of examined bones within the breeds took the following order: Pietrain race&gt; line 990&gt; Duroc race. The differences between the “extreme” breeds were: 7.2% (III metatarsal bone), 8.3% (III metacarpal bone), 8.4% (femur). Abstract 13 The average bone stiffness took the following order: III metatarsal bone (35.1 kg/mm) &lt;III metacarpus (41.5 kg/mm) &lt;femur (60.5 kg/mm). This indicator did not differ between the groups of pigs fed at different intensity, except for the metacarpal bone, which was more stiffer in pigs fed aa libitum (P&lt;0.05). The femur of animals fed ad libitum showed a tendency (P&lt;0.09) to be more stiffer and a force of 4.5 kg required for its displacement by 1 mm. Breed differences in stiffness were found for the femur (P &lt;0.05) and III metacarpal bone (P &lt;0.05). For femur, the highest value of this indicator was found in Pietrain pigs (64.5 kg/mm), lower in pigs of 990 line (61.6 kg/mm) and the lowest in Duroc pigs (55.3 kg/mm). In turn, the 3rd metacarpal bone of Duroc and Pietrain pigs had similar stiffness (39.0 and 40.0 kg/mm respectively) and was smaller than that of line 990 pigs (45.4 kg/mm). The thickness of the cortical bone layer took the following order: III metatarsal bone (2.25 mm) &lt;III metacarpal bone (2.41 mm) &lt;femur (5.12 mm). The feeding system did not affect this indicator. Breed differences (P &lt;0.05) for this trait were found only for the femur bone: Duroc (5.42 mm)&gt; line 990 (5.13 mm)&gt; Pietrain (4.81 mm). The cross sectional area of the examined bones was arranged in the following order: III metatarsal bone (84 mm2) &lt;III metacarpal bone (90 mm2) &lt;femur (286 mm2). The feeding system had no effect on the value of this bone trait, with the exception of the femur, which in animals fed the dosing system was 4.7% higher (P&lt;0.05) than in pigs fed ad libitum. Breed differences (P&lt;0.01) in the coross sectional area were found only in femur and III metatarsal bone. The value of this indicator was the highest in Duroc pigs, lower in 990 animals and the lowest in Pietrain pigs. The cortical index of individual bones was in the following order: III metatarsal bone (31.86) &lt;III metacarpal bone (33.86) &lt;femur (44.75). However, its value did not significantly depend on the intensity of feeding or the breed of pigs.
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Book chapters on the topic "The k-average method"

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Dobša, Jasminka, and Henk A. L. Kiers. "Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_14.

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AbstractIn the paper we propose a method for representation of documents in a semantic lower-dimensional space based on the modified Reduced k-means method which penalizes clusterings that are distant from classification of training documents given by experts. Reduced k-means (RKM) enables simultaneously clustering of documents and extraction of factors. By projection of documents represented in the vector space model on extracted factors, documents are clustered in the semantic space in a semi-supervised way (using penalization) because clustering is guided by classification given by experts, which enables improvement of classification performance of test documents. Classification performance is tested for classification by logistic regression and support vector machines (SVMs) for classes of Reuters-21578 data set. It is shown that representation of documents by the RKM method with penalization improves the average precision of classification by SVMs for the 25 largest classes of Reuters collection for about 5,5% with the same level of average recall in comparison to the basic representation in the vector space model. In the case of classification by logistic regression, representation by the RKM with penalization improves average recall for about 1% in comparison to the basic representation.
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Zhang, Junxiong, Yu Zhang, Jinyi Xie, et al. "Panoptic Semantic Mapping Method for Tomato Growing Environment Based on K-Net and OctoMap." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2409-6_18.

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Abstract In modern greenhouses, complicated tasks and unstructured environments generate the imperious demand for advanced semantic information about each object at work scenes. A significant problem that mainstream methods intend to resolve is that the refinement and understanding of environmental information cannot efficiently cover the entire task in real time. Therefore, this paper proposes a panoptic semantic mapping method to identify each object that is supposed to be concerned in greenhouses. This method builds grid maps with advanced semantic information based on RGB and depth images. For the agricultural task with tomato as the working object, the categories of various objects in the grid map are divided into four groups: fruits, pedicels, stems and obstacles. This method consists of three steps: semantic segmentation from RGB images with K-Net, reconstruction of point cloud data based on depth images and semantic masks and transformation of the point cloud data into OctoMap. Experimental results show the semantic segmentation algorithm reaches a mean precision of semantic segmentation of 93.83%, a mean IoU of 88.39% and an average accuracy of 98.28%. Meanwhile, the refresh frequency of publishing point cloud data with advanced semantic information holds steady at 2 Hz with the resolution of 8 mm.
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Wang, Jingchu, Jianyi Liu, Feiyu Chen, Teng Lu, Hua Huang, and Jinmeng Zhao. "Cross-Knowledge Graph Entity Alignment via Neural Tensor Network." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_8.

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AbstractWith the expansion of the current knowledge graph scale and the increase of the number of entities, a large number of knowledge graphs express the same entity in different ways, so the importance of knowledge graph fusion is increasingly manifested. Traditional entity alignment algorithms have limited application scope and low efficiency. This paper proposes an entity alignment method based on neural tensor network (NtnEA), which can obtain the inherent semantic information of text without being restricted by linguistic features and structural information, and without relying on string information. In the three cross-lingual language data sets DBPFR−EN, DBPZH−EN and DBPJP−EN of the DBP15K data set, Mean Reciprocal Rank and Hits@k are used as the alignment effect evaluation indicators for entity alignment tasks. Compared with the existing entity alignment methods of MTransE, IPTransE, AlignE and AVR-GCN, the Hit@10 values of the NtnEA method are 85.67, 79.20, and 78.93, and the MRR is 0.558, 0.511, and 0.499, which are better than traditional methods and improved 10.7% on average.
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Wenka, T. "On the Role of k in Depth-Averaged k-ε Turbulence Modelling." In Computational Methods in Water Resources X. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-010-9204-3_143.

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Okumura, Taiga, Noriko Yamaguchi, and Toshihiro Kogure. "Structure, Composition, and Physicochemical Properties of Radiocesium-Bearing Microparticles Emitted by the Fukushima Daiichi Nuclear Power Plant Accident." In Agricultural Implications of Fukushima Nuclear Accident (IV). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9361-9_8.

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AbstractDuring the accident at TEPCO’s Fukushima Daiichi Nuclear Power Plant, radiocesium-bearing microparticles (CsMPs) were released from damaged reactors into the environment. These micron-sized spherical particles with high specific radioactivity have not been reported in previous nuclear accidents. Herein, the current understanding of the structure, composition, and physicochemical properties of CsMPs is summarized. Electron microscopy revealed that the CsMP matrix is composed of silicate glass containing Na, Cl, K, Fe, Zn, Rb, Sn, and Cs as major constituents. These elements are often inhomogeneously distributed, depending on the particle radius, and Cs was concentrated around the outer side of the particles. In addition, nanocrystals including Cr-rich oxides and chalcogenides were frequently found inside CsMPs. The average valence state of Fe in the CsMP glass matrix was almost Fe2+, indicating formation under a reducing atmosphere through condensation from the gas phase. Radiocesium diffused away from the CsMPs when heated to &gt;600 °C. Accordingly, CsMPs may lose their high specific radioactivity when related radiation-contaminated waste is incinerated at sufficiently high temperatures. Although CsMP solubility is low, they cannot be regarded as “insoluble” materials owing to their small size. CsMP dissolution rates depend on the pH and dissolved species in the solution, and their dissolution behavior is comparable to that of silica-rich glass. Based on these dissolution properties, a method for estimating CsMP abundance and spatial distribution in the environment was proposed. The findings detailed herein contribute to the comprehensive elucidation of CsMP environmental dynamics.
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Deville, Michel O. "Turbulence." In An Introduction to the Mechanics of Incompressible Fluids. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04683-4_9.

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AbstractThe Reynolds decomposition and statistical averaging of velocity and pressure generate the Reynolds averaged Navier–Stokes (RANS) equations. The closure problem is solved by the introduction of a turbulence constitutive equation. Several linear turbulence models are presented in the RANS framework: $$K-\varepsilon , K-\omega $$ K - ε , K - ω . The solution of the RANS equations for the turbulent channel flow is elaborated giving the celebrated logarithmic profile. Non-linear models are built on the anisotropy tensor and the incorporation of the concept of integrity bases. The chapter ends with the theory of large eddy simulations with a few up-to-date models: dynamic model, approximate deconvolution method.
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Ou, Zhixin, Juan Chen, Yuyang Sun, et al. "AOA: Adaptive Overclocking Algorithm on CPU-GPU Heterogeneous Platforms." In Algorithms and Architectures for Parallel Processing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-22677-9_14.

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AbstractAlthough GPUs have been used to accelerate various convolutional neural network algorithms with good performance, the demand for performance improvement is still continuously increasing. CPU/GPU overclocking technology brings opportunities for further performance improvement in CPU-GPU heterogeneous platforms. However, CPU/GPU overclocking inevitably increases the power of the CPU/GPU, which is not conducive to energy conservation, energy efficiency optimization, or even system stability. How to effectively constrain the total energy to remain roughly unchanged during the CPU/GPU overclocking is a key issue in designing adaptive overclocking algorithms. There are two key factors during solving this key issue. Firstly, the dynamic power upper bound must be set to reflect the real-time behavior characteristics of the program so that algorithm can better meet the total energy unchanging constraints; secondly, instead of independently overclocking at both CPU and GPU sides, coordinately overclocking on CPU-GPU must be considered to adapt to real-time load balance for higher performance improvement and better energy constraints. This paper proposes an Adaptive Overclocking Algorithm (AOA) on CPU-GPU heterogeneous platforms to achieve the goal of performance improvement while the total energy remains roughly unchanged. AOA uses the function $$F_k$$ F k to describe the variable power upper bound and introduces the load imbalance factor W to realize the CPU-GPU coordinated overclocking. Through the verification of several types convolutional neural network algorithms on two CPU-GPU heterogeneous platforms (Intel$$^\circledR $$ ® Xeon E5-2660 &amp; NVIDIA$$^\circledR $$ ® Tesla K80; Intel$$^\circledR $$ ® Core™i9-10920X &amp; NIVIDIA$$^\circledR $$ ® GeForce RTX 2080Ti), AOA achieves an average of 10.7% performance improvement and 4.4% energy savings. To verify the effectiveness of the AOA, we compare AOA with other methods including automatic boost, the highest overclocking and static optimal overclocking.
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Önay, Onur. "Clustering by K-Means Method and K-Medoids Method." In Advances in Data Mining and Database Management. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3053-5.ch024.

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Data science and data analytics are becoming increasingly important. It is widely used in scientific and real-life applications. These methods enable us to analyze, understand, and interpret the data in every field. In this study, k-means and k-medoids clustering methods are applied to cluster the Statistical Regions of Turkey in Level 2. Clustering analyses are done for 2017 and 2018 years. The datasets consist of “Distribution of expenditure groups according to Household Budget Survey” 2017 and 2018 values, “Gini coefficient by equivalised household disposable income” 2017 and 2018 values, and some features of “Regional Purchasing Power Parities for the main groups of consumption expenditures” 2017 values. Elbow method and average silhouette method are applied for the determining the number of the clusters at the beginning. Results are given and interpreted at the conclusion.
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K. Belyaev, Yury, and Asaf H. Hajiyev. "Estimation of the Efficiency Indices for Operating the Vertical Transportation Systems." In Smart Cities [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94066.

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Various lifts’ systems with different control rules are considered. It is suggested to use the efficiency indexes: customer’s average waiting in lift cabin time and average total time, including the time of delivering the customer to the desired floor. Various control rules are introduced: Odd-Even, where one lift serves only customers in Odd floors and other lift only does that in Even floors Up-Down control rule where one lift serves only customers who are going from the first floor to the destination floor 2, 3,…, k; another lift serves customers from the first floor to the upper floor k + 1, k + 2, …, n. The results of simulation, allowing to compare various control rules relatively to the efficiency indexes, are given. It is introduced an optimal number of lifts, which minimizes number of lifts, minimizing a customer’s average waiting time. For some systems, the method of finding the optimal number of lifts, is suggested. Necessary figures demonstrating the operation of the lifts’ systems and the results of the simulation allow to estimate the efficiency indexes.
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Ilyas, F. Mohamed, and S. Silvia Priscila. "An Optimized Clustering Quality Analysis in K-Means Cluster Using Silhouette Scores." In Explainable AI Applications for Human Behavior Analysis. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1355-8.ch004.

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Data-driven problem-solving requires the capacity to use cutting-edge computational methods to explain fundamental phenomena to a large audience. These facilities are needed for political and social studies. Quantitative methods often involve knowledge of concepts, trends, and facts that affect the study programme. Researchers often don't know the data's structure or assumptions when analysing it. Data exploration may also obscure social science research methodology instruction. It was essential applied research before predictive modelling and hypothesis testing. Clustering is part of data mining and picking the right cluster count is key to improving predictive model accuracy for large datasets. Unsupervised machine learning (ML) algorithm K-means is popular. The method usually finds discrete, non-overlapping clusters with groups for each location. It can be difficult to choose the best k-means approach. In the human freedom index (HFI) dataset, the mini batch k-mean (MBK-mean) using the Hamely method reduces iteration and increases cluster efficiency. The silhouette score algorithm from Scikit-learn was used to obtain the average silhouette co-efficient of all samples for various cluster counts. A cluster with fewer negative values is considered best. Additionally, the silhouette with the greatest score has the optimum clusters.
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Conference papers on the topic "The k-average method"

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Eriksson, Amelie, Kristina Lund, and Katarina Persson. "Electrochemical Corrosion Comparison of Seamless Tubes in UNS S31266 and UNS S31254 with New Testing Method." In CORROSION 2018. NACE International, 2018. https://doi.org/10.5006/c2018-11092.

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Abstract The super-austenitic stainless steel UNS S31266 is due to its optimum of alloying elements a suitable material for seawater applications as for example seawater cooled heat exchangers. Producing UNS S31266 as seamless tubes gives the possibility to build heat exchangers with both tubes and plates in UNS S31266. The super-austenitic stainless steel UNS S31254 also has excellent corrosion resistance in a variety of industrial environments. In this study the critical pitting temperature (CPT) for UNS S31266 was compared to UNS S31254 with the newly developed electrochemical method, modified ASTM G150 developed by K. Lund et al., using 3M magnesium chloride (MgCl2) instead of 1M sodium chloride (NaCl). CPT-values measured by the modified ASTM G150-method were shown to be 10-20 °C lower than when using 1M NaCl. Also, the crevice corrosion resistance of UNS S31266 was compared to UNS S31254 measured in NaCl solution. The average CPTmod in 3M MgCl2 for UNS S31266 was 86 °C and for UNS S31254 it was 66 °C. The CPTmod was thus 20 °C higher for the UNS S31266 seamless tubes compared to UNS S31254. The CCT in NaCl of UNS S31266 was approximately 85 °C and for UNS S31254 it was approximately 60 °C.
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Zhao, Yang, and Bi Zeng. "An improved k-means algorithm based on average diameter method." In GREEN ENERGY AND SUSTAINABLE DEVELOPMENT I: Proceedings of the International Conference on Green Energy and Sustainable Development (GESD 2017). Author(s), 2017. http://dx.doi.org/10.1063/1.4992871.

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Xu, Qian, Xueqi Shen, Rui Jia, and Chun Ren. "A Fast Method to Simulate the Average K-factor in a Reverberation Chamber." In 2022 Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC). IEEE, 2022. http://dx.doi.org/10.1109/apemc53576.2022.9888576.

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4

Ravikumar, P. "Generation of Monthly Average Hourly Ambient Temperatures From Monthly Average Daily Temperature." In ASME 2004 International Solar Energy Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/isec2004-65137.

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A method to generate monthly average hourly ambient temperature values, Tah,n, in terms of the monthly average daily ambient temperature, Ta and latitude, φ is presented here. The present correlations do not require the additional information needed in applying the correlations available in [1,2,3]. The predicted monthly average hourly ambient temperature, Tah,n values have been found to be in agreement with data values within a % rms difference of 0.43 (when Tah,n is in K) and the standard deviation has been found to be 1.243 K, for 56 primary locations of TMY2, comparable to the prediction of Erbs, Klein and Beckman [1].
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Hsiao, Tsai-Wen, Tsang-Pin Chang, Hsi-Tseng Chou, and Shih-Chung Tuan. "A novel moving average method of vehicle detection in the FMCW radar using antennas with different beamwidths at K-band." In 2015 IEEE 6th International Symposium on Microwave, Antenna, Propagation, and EMC Technologies (MAPE). IEEE, 2015. http://dx.doi.org/10.1109/mape.2015.7510284.

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Huang, Junyu, Qilong Feng, Ziyun Huang, Jinhui Xu, and Jianxin Wang. "FLS: A New Local Search Algorithm for K-means with Smaller Search Space." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/429.

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The k-means problem is an extensively studied unsupervised learning problem with various applications in decision making and data mining. In this paper, we propose a fast and practical local search algorithm for the k-means problem. Our method reduces the search space of swap pairs from O(nk) to O(k^2), and applies random mutations to find potentially better solutions when local search falls into poor local optimum. With the assumption of data distribution that each optimal cluster has "average" size of \Omega(n/k), which is common in many datasets and k-means benchmarks, we prove that our proposed algorithm gives a (100+\epsilon)-approximate solution in expectation. Empirical experiments show that our algorithm achieves better performance compared to existing state-of-the-art local search methods on k-means benchmarks and large datasets.
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Rodrigues, Natan S., and Celia G. Ralha. "A Hybrid Machine Learning Method to Author Name Disambiguation." In Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Sociedade Brasileira de Computação, 2024. https://doi.org/10.5753/stil.2024.245440.

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Digital bibliographic repositories, including publications, authors, and research fields are essential for sharing scientific information. Nevertheless, the information retrieval, extraction, and classification efficiency in such archives is threatened by author name ambiguity. This paper addresses the Author Name Disambiguation (AND) problem by proposing a hybrid machine learning method integrating Bidirectional Encoder Representations from Transformers (BERT), Graph Convolutional Network (GCN), and Graph Enhanced Hierarchical Agglomerative Clustering (GHAC) approaches. The BERT model extracts textual data from scientific documents, the GCN structures global data from academic graphs, and GHAC considers heterogeneous networks’ global context to identify scientific collaboration patterns. We compare the hybrid method with AND state-of-the-art work using a publicly accessible data set consisting of 7,886 documents, 137 unique authors, and 14 groups of ambiguous authors, along with recognized validation metrics. The results achieved a high precision score of 93.8%, recall of 96.3%, F1-measure of 95%, Average Cluster Purity (ACP) of 96.5%, Average Author Purity (AAP) of 97.4% and K-Metric of 96.9%. Compared to the AND baseline approach, the hybrid method presents better results indicating a promising approach.
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Zhou, Yang, Yichang Shao, Zhongyi Han, and Zhirui Ye. "Dynamic Evaluation Method for Public Transport Operation Indicators Based on Rank Transformation." In 2024 International Conference on Smart Transportation Interdisciplinary Studies. SAE International, 2025. https://doi.org/10.4271/2025-01-7219.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;This study presents a method to evaluate the daily operation of traditional public transportation using multi-source data and rank transformation. In contrast with previous studies, we focuses on dynamic indicators generated during vehicle operation, while ignoring static indicators. This provides a better reference value for the daily operation management of public transport vehicles. Initially, we match on-board GPS data with network and stop coordinates to extract arrival and departure timetable. This helps us calculate dynamic operational metrics such as dwell time, arrival interval, and frequency of vehicle bunching and large interval. By integrating IC card data with arrival timetable, we can also estimate the number of people boarding at each stop and derive passenger arrival time, waiting time, and average waiting time. Finally, we developed a comprehensive dynamic evaluation method of public transportation performance, covering the three dimensions: bus stops, vehicles, and routes. This method uses K-means clustering to classify and applies rank transformation techniques to score. At stop levels, we use principal component analysis(PCA) to identify key influencing factors, anf apply K-means for clustering and service-level classification. At the vehicle and route level, we perform rank transformation on indicators such as average waiting time and vehicle bunching frequency. Delphi method is used to determine the relative weights of each indicator, so as to facilitate the ranking of bus routes according to the comprehensive score. This method is applicable to the dynamic operation indicators of 20 bus routes in Shenzhen, involving 293 vehicles and 506 stops. The results show that this method can effectively evaluate the dynamic operation of public transport and make contribution to daily management.&lt;/div&gt;&lt;/div&gt;
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Yang, Qingyuan, and Hu Ding. "Approximate Algorithms for k-Sparse Wasserstein Barycenter with Outliers." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/588.

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Wasserstein Barycenter (WB) is one of the most fundamental optimization problems in optimal transportation. Given a set of distributions, the goal of WB is to find a new distribution that minimizes the average Wasserstein distance to them. The problem becomes even harder if we restrict the solution to be “k-sparse”. In this paper, we study the k-sparse WB problem in the presence of outliers, which is a more practical setting since real-world data often contains noise. Existing WB algorithms cannot be directly extended to handle the case with outliers, and thus it is urgently needed to develop some novel ideas. First, we investigate the relation between k-sparse WB with outliers and the clustering (with outliers) problems. In particular, we propose a clustering based LP method that yields constant approximation factor for the k-sparse WB with outliers problem. Further, we utilize the coreset technique to achieve the (1+ε)-approximation factor for any ε&gt;0, if the dimensionality is not high. Finally, we conduct the experiments for our proposed algorithms and illustrate their efficiencies in practice.
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Wanderley, Juan B. V., Gisele H. B. Souza, and Carlos Levi. "Effect of Turbulence Modeling on VIV Numerical Simulation." In ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67073.

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Author’s previous work Wanderley [1] presented an efficient numerical method to investigate VIV phenomenon on circular cylinders. The numerical model solves the unsteady Reynolds Average Navier–Stokes equations for slightly compressible flows using the Beam–Warming implicit factored scheme. In the present work, the effect of the turbulence model on the results is evaluated for both Baldwin Lomax and k-ε models. To demonstrate the quality of the numerical method, results for the transversal oscillation of a cylinder laterally supported by spring and damper are compared with experimental data. The application of the turbulence models showed the much better agreement of the k-ε model with the experimental results.
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